Experimental setup. To train and validate our classification teachers, we use the MS COCO2017 [1]} detection dataset and crop all the objects according to their ground-truth bounding boxes. The resulting classification dataset consists of 849,902 objects for training and 36,334 objects for validation. We then train the teacher models in an image-classification manner, using the same data augmentation strategy and loss function as the student detector. Specifically, Faster RCNNs and RetinaNets share the same data augmentation methods, denoted as “general”, but use the categorical cross-entropy loss (CEL) and focal loss (FL) for their classification heads, respectively; SSDs have their own data augmentation strategy and use the categorical cross-entropy loss (CEL).
